Stochastic Processing

ABSTRACT

A system, method, and device for stochastically processing data. There is an architect module operating on a processor configured to manage and control stochastic processing of data, a non-deterministic data pool module configured to provide a stream of non-deterministic values that are not derived from a function, a plurality of functionally equivalent data processing modules each configured to stochastically process data as called upon by the architect module, a data feed configured to feed a data set desired to be stochastically processed, and a structure memory module including a memory storage device and configured to provide sufficient information for the architect module to duplicate a predefined processing architecture and to record a utilized processing architecture.

CROSS-REFERENCE TO RELATED APPLICATIONS

This invention claims priority, under 35 U.S.C. §120, to the U.S.Provisional Patent Application No. 61/519,679 to Patrick Ross filed onMay 27, 2011, which is incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods and systems for protectinginformation, specifically to methods and systems to produce dynamicapplications that provide stochastic processing of information.

2. Description of the Related Art

Many applications require random values as part of their internalprocessing. Some of these applications have simple requirements like auniform distribution of values, reproducibility from a given seed value,and very long cycles before they repeat. To that end, many papers andbooks describe good hardware and software functions that provide theseclassic random value generators. The attributes of classic random valuegenerators remain both useful and problematic. Before addressing anyshortcomings of current random value generators, we generally must firstreview how these generators work.

There exists a number of properties common to all classic random valuegenerators, whether they be hardware or software based. The cornerstoneof classic random value generators is the use of static randomfunctions. Each of these functions processes the current non-zero datavalue into the next random value in the sequence. The subsequentprocessing of each new value creates the random sequence. Assuming thata good function is used, the random sequence will pass almost all knownstatistical tests for randomness.

Numerous random functions have been tested and published. Most of thesepublished functions produce a limited sequence of values beforerepeating the same sequence of random values. These brief cycle lengthsmay be too short to be compatible with many applications. In hardware,the random functions are most often described as Linear Feedback ShiftRegisters (LFSR). Though fewer software functions exist, a number ofestablished functions are available for the designer to use in newapplications. Also, most software random functions share the same shortcycle attribute.

Whether passing or failing, cycle length proves just as important asstatistical testing. Combining multiple published functions in anon-linear manner is the most common way to increase cycle length. Thefunction-based random value generators are correctly called pseudorandomgenerators and remain easy to “crack” (invert). Cracking a random valuegenerator allows an attacker to anticipate each of the values in thesequence. As a rule of thumb, doubling the classic random functioncomplexity has the effect of squaring the effort required to crack it.As the speed of hardware and therefore computers increases, the battlebecomes an arms race between the designer of random value generators andthe cracker wishing to break them.

Embracing this rule of thumb, hardware designers adopt evermore complexrandom value generator functions. The struggle between the designer andcracker persists because the function driven paradigm is inevitablyincomplete. The cost to create, test, and deploy new random valuegenerators is thereby open-ended, because each new design is destined tobecome obsolete. Subsequently, higher recurring chip costs translatedirectly into product costs. The endpoint along this path is unknown, soa designer cannot anticipate how long their newest function will provesafe from cracking.

The costs of increasing function complexity are manifested in multipleways. As noted above, the hardware arms race persists as an inevitablyincomplete problem. Each new jump in hardware technology generallyrequires a new corresponding generator design in order to stay ahead ofthe crackers. This escalating cost forces many application designers toforgo the hardware-based solution. To cut system cost, many applicationdesigners resort to software-based random value generators. Often theprocess of transitioning to a software solution either slows performanceunacceptably or increases CPU costs. While the recurring costs are lowerwithout dedicated silicon, the software implementation taxes overallsystem performance. As the software complexity increases, performanceinversely decreases.

In an effort to resist cracking, many designers resort to secret(non-public) designs. Development in secrecy necessitates limitedtesting, review, or reuse. Unfortunately, secret development guaranteesa limited return on investment because low volume of a given designgenerally always carries higher cost per unit. Furthermore, secrecy onlysustains the integrity of these designs until someone obtains a hardwareor software example.

The final weakness to these classic functions stems from a simpleimmutable fact: each random function produces its own random sequence.Stated another way, there is a one-to-one correspondence between therandom function and the unique sequence of values it produces. Thatsequence acts like a “melody” with respect to its generating function. Arandom “melody” is defined as both the values and the order of thosevalues as they are produced. The seed value only defines where the“melody” starts.

All classic random value generators use a scalar value (startingnon-zero seed) to index the point at which their unique “melody” begins.Since classic random value generators are static function-basedconstructs, the seed value generally must be protected because it actsas the key to define the start of the pseudorandom sequence. In mostcases, the size of the seed value is used to indicate the overall cyclelength. All hardware and most software based classic random valuegenerators require a non-zero seed value to start generating randomvalues. In almost all cases, a zero value seed will fail to generate anyrandom stream.

In a futile effort to resist cracking, many designers resort to secret(non-public) designs. Development in secrecy necessitates limitedtesting, review, or reuse. Unfortunately, secret development guaranteesa limited return on investment because low volume of a given designgenerally always carries higher cost per unit. Furthermore, secrecy onlysustains the integrity of these designs until someone obtains a hardwareor software example. What is needed is a true random value generator,one that implements a true one way function, resulting in a randomstream of values that is non-deterministic and/or a method or systemthat solves one or more of the problems described herein and/or one ormore problems that may come to the attention of one skilled in the artupon becoming familiar with this specification. Some improvements havebeen made in the field. Examples of references related to the presentinvention are described below in their own words, and the supportingteachings of each reference are incorporated by reference herein:

U.S. Patent Application Publication No.: 2011/0029588, by Ross,discloses a system and method of generating a one-way function andthereby producing a random-value stream. Steps include: providing aplurality of memory cells addressed according to a domain value whereinany given domain value maps to all possible range values; generating arandom domain value associated with one of the memory cells; reading adata value associated with the generated random domain value; generatingdynamically enhanced data by providing an additional quantity of data;removing suspected non-random portions thereby creating source data;validating the source data according to a minimum randomnessrequirement, thereby creating a validated source data; and integratingthe validated source data with the memory cell locations using a randomedit process that is a masking, a displacement-in-time, a chaos engine,an XOR, an overwrite, an expand, a remove, a control plane, or anaddress plane module. The expand module inserts a noise chunk.

U.S. Patent Application Publication No.: 2010/0036900, by Ross,discloses a system and method of generating a one-way function andthereby producing a random-value stream. Steps include: providing aplurality of memory cells addressed according to a domain value whereinany given domain value maps to all possible range values; generating arandom domain value associated with one of the memory cells; reading adata value associated with the generated random domain value; generatingdynamically enhanced data by providing an additional quantity of data;removing suspected non-random portions thereby creating source data;validating the source data according to a minimum randomnessrequirement, thereby creating a validated source data; and integratingthe validated source data with the memory cell locations using a randomedit process that is a masking, a displacement-in-time, a chaos engine,an XOR, an overwrite, an expand, a remove, a control plane, or anaddress plane module. The expand module inserts a noise chunk.

The inventions heretofore known suffer from a number of disadvantageswhich include being difficult to use, being complex, being expensive,being limited in use, being limited in application, being unreliable,being determinable, being certain, requiring ever larger periods ofprocessing time for subsequent sets of random data, failing to be true“one-way” functions, having vulnerabilities and weaknesses that make iteasier for unauthorized users to decrypt information, and the like andcombinations thereof.

What is needed is a method, system, apparatus, device, computer program,kit, and/or combination thereof that solves one or more of the problemsdescribed herein and/or one or more problems that may come to theattention of one skilled in the art upon becoming familiar with thisspecification.

The following and/or accompanying disclosure information is provided asnon-limiting examples of features, functions, structures, associations,connections, methods, steps, benefits, consequences, and the like thatmay be included independently, in any open combination, and in anylimited combinational form (consisting of) despite any language to thecontrary, such as but not limited to “must” “always” “never” “certainly”and the like. Any dimensions provided are exemplary and functionallyequivalent ranges that one skilled in the art may recognize afterreading this disclosure are implied. Disclosure provided may beprophetic, even if asserted as otherwise.

SUMMARY OF THE INVENTION

The present invention has been developed in response to the presentstate of the art, and in particular, in response to the problems andneeds in the art that have not yet been fully solved by currentlyavailable static, (and therefore deterministic) hardware and softwaresolutions. Accordingly, the present invention has been developed toprovide a method and/or a system of generating dynamic, nondeterministicsolutions in either hardware or software, including but not limited to asystem, method and/or device for stochastic processing of information.

According to one embodiment of the invention, there is a system ofstochastic processing of information using a computing device. Thesystem may include an architect module that may have a processor. Thearchitect module may be configured to manage and control stochasticprocessing of data. The architect module may include a run-timemodification module that may be configured to randomly alter astochastic architecture during run-time. The run-time modificationmodule may be seeded from the non-deterministic data pool module. Thearchitect module may be configured to use random values to selectbetween the plurality of functionally equivalent data processing modulesduring run-time. The architect module may use random values to selectrun-time durations for use of one of the plurality of functionallyequivalent data processing modules during run-time.

The system may include a non-deterministic data pool module that may befunctionally coupled to the architect module and may be configured toprovide a stream of non-deterministic values that are not derived from afunction. The non-deterministic data pool module may include a URNGsystem. The system may include a plurality of functionally equivalentdata processing modules that may be functionally coupled to thearchitect module, and each may be configured to stochastically processdata as called upon by the architect module.

The system may include a data feed module that may be in functionalcommunication with the architect module and may be configured to feed adata set desired to be stochastically processed. The system may includea structure memory module that may have a memory storage device. Thestructure memory module may be coupled to the architect module and maybe configured to provide sufficient information for the architect moduleto duplicate a predefined processing architecture and to record autilized processing architecture. The structure memory module mayinclude an index module that indexes structure according to a timestructure.

The system may include a common data pool processing module that may befunctionally coupled to the non-deterministic data pool module and maybe configured to stochastically process a common non-deterministic datapool thereby generating an application specific non-deterministic datapool for use by the non-deterministic data pool module. The system mayinclude a communication protocol interface that may be in communicationwith the data feed module and may be configured to feed a communicationprotocol map to the data feed module, receive a stochastically processedcommunication protocol map from the architect module, and to alter aninformation stream according to the stochastically processedcommunication protocol map.

According to one embodiment of the invention, there is a method ofstochastically processing information using a computing device. Themethod may include the step of providing a non-deterministic data poolthat is verified to be non-deterministic and is not derived from afunction. The method includes verifying that the non-deterministic datapool passes the NIST test with a predominant 10/10 score. The method mayinclude the step of providing an information stream to be processed. Themethod may include the step of delaying selection of all randomized dataprocessing characteristics until run-time.

The method may include randomly selecting a first data processingmodule, using a processor, from a set of functionally equivalent dataprocessing modules, each configured to alter data. The method mayinclude the step of determining a random duration of use of the firstdata processing module during run-time. The set of functionallyequivalent data processing modules may be selected from the group ofdata processing modules including: subtraction, masking, NAND, NOR, OR,XOR, AND, and addition. The method may include seeding a step ofrandomly selecting a data processing module from the non-deterministicdata pool.

The method of stochastically processing information using a computingdevice may include the step of altering the information stream by use ofthe first data processing module. The method may include randomlyselecting a replacement data processing module, using a processor, fromthe set of functionally equivalent data processing modules whileprocessing the information stream with the first data processing module.The method may also include replacing the first data processing modulewith the replacement data processing module.

The method may include the step of altering the information stream byuse of the replacement data processing module. The information streammay be configured according to a predefined communication protocol andthe first and replacement data processing modules each may sufficientlyprocess the information stream to make the information stream fail tosatisfy the requirements of the predefined communication protocol. Themethod may include randomly layering use of a plurality of dataprocessing modules such that the information stream is processed throughmultiple randomized layers of data processing modules.

The method may further include the step of recording structureinformation sufficient to reproduce use of the first and replacementdata processing modules. The method may include associating operation ofthe method with a time index such that operation of the method bycounterparts beginning with identical time index positions and anidentical non-deterministic data pool may process the information streamidentically. The method may include the step of stochasticallyprocessing the non-deterministic data pool before utilization of thenon-deterministic data pool. The method may also include the step ofmanaging randomization such that each call to a source of random valuesgoes to a different source than each previous call.

According to one embodiment of the invention, there is a stochasticprocessing device configured to stochastically process information fedtherein. The device may include a processor and a non-volatile memorydevice that may be functionally coupled to the processor. Thenon-volatile memory device may include a pool of non-deterministic datathat may be verified to have passed the NIST test with a predominant10/10 score. The device may include a data input interface module thatmay be functionally coupled to the processor and may be configured toreceive data. The device may include a data output interface module thatmay be functionally coupled to the processor and may be configured tosend data.

The device may also include a data processing module that may befunctionally coupled to the processor and may include a plurality offunctionally equivalent data processing instruction sets. The device mayinclude an architect module that may be functionally coupled to theprocessor, the data processing module and to the non-volatile memorydevice. The architect module may be configured to manage and controlstochastic processing of data according to seed values from the pool ofnon-deterministic data by randomly selecting data processing modulesduring run-time, thereby processing data received through the data inputinterface module and providing stochastically processed data to the dataoutput interface module.

In one embodiment, a single, formerly static solution is transformedinto many dynamic custom solutions within the same implementation. Thisnew genome of solutions is based on a number of new technologies,including but not limited to one or more of the following modules:

Uncertainty Function—One Way Function

Uncertainty Random Number Generators

Dynamic Selection of Processing Components

Delayed Binding of Components Until Needed

On Demand Remixing of Components

Data Driven Implementations

Embodiments of this new genome of dynamic solutions simplify manypreferred solutions. The replacement of static, “one size fits all”applications with custom solutions resolve many currently unsolvedproblems.

In another embodiment, all of these new techniques come together as anopen-ended architectural solution for generating random values. Thiskind of architectural model scales from very low cost products toextremely demanding applications, based on their random datarequirements. Thus, we arrive at data morphing data instead of functionsprocessing data.

In still another embodiment, there is a method of morphing staticprotocols into evolving protocols. Custom, dynamically evolvingprotocols become impossible to hack.

In still another embodiment, a simple hash value can be upgraded tobecome secure digital signatures. Assuming “service providers” onlysupport these secure digital signatures, then any unauthorized hashvalues are ignored. Thus, these digital signatures become a form ofrevocable access control. This approach to access control can extend toany item, phone number, email address, IP address, control system,financial transaction, etc.

In still another embodiment, a correctly implemented example of thecustom solutions will be harder to invert than the same cost staticsolutions, assuming the session data (to be defined later) has not beencompromised. This means that everyone can use the same publichardware/software implementation yet still have the same resistance tocracking.

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present invention should be or are in anysingle embodiment of the invention. Rather, language referring to thefeatures and advantages is understood to mean that a specific feature,advantage, or characteristic described in connection with an embodimentis included in at least one embodiment of the present invention. Thus,discussion of the features and advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

These features and advantages of the present invention will become morefully apparent from the following description and appended claims, ormay be learned by the practice of the invention as set forthhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order for the advantages of the invention to be readily understood, amore particular description of the invention briefly described abovewill be rendered by reference to specific embodiments that areillustrated in the appended drawing(s). It is noted that the drawings ofthe invention are not to scale. The drawings are mere schematicsrepresentations, not intended to portray specific parameters of theinvention. Understanding that these drawing(s) depict only typicalembodiments of the invention and are not, therefore, to be considered tobe limiting its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawing(s), in which:

FIG. 1 is a flowchart depicting a selecting process of elements for aninstance of stochastic module/process, according to one embodiment ofthe invention;

FIG. 2 is a flowchart depicting a testing of the given processingelement, according to one embodiment of the invention;

FIG. 3 illustrates a system of stochastic processing of informationaccording to one embodiment of the invention;

FIGS. 4-5 illustrate a method of stochastically processing informationusing a computing device; and

FIG. 6 illustrates a stochastic processing device configured tostochastically process information fed therein.

DETAILED DESCRIPTION OF THE INVENTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the exemplary embodimentsillustrated in the drawing(s), and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the invention is thereby intended. Any alterations andfurther modifications of the inventive features illustrated herein, andany additional applications of the principles of the invention asillustrated herein, which would occur to one skilled in the relevant artand having possession of this disclosure, are to be considered withinthe scope of the invention.

Many of the functional units described in this specification have beenlabeled as modules, in order to more particularly emphasize theirimplementation independence. For example, a module may be implemented asa hardware circuit comprising custom VLSI circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A module may also be implemented in programmablehardware devices such as field programmable gate arrays, programmablearray logic, programmable logic devices or the like.

Any of the functions, features, benefits, structures, and etc. describedherein may be embodied in one or more modules. Many of the functionalunits described in this specification have been labeled as modules, inorder to more particularly emphasize their implementation independence.For example, a module may be implemented as a hardware circuitcomprising custom VLSI circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices or the like.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of programmable or executablecode may, for instance, comprise one or more physical or logical blocksof computer instructions which may, for instance, be organized as anobject, procedure, or function. Nevertheless, the executables of anidentified module need not be physically located together, but maycomprise disparate instructions stored in different locations which,when joined logically together, comprise the module and achieve thestated purpose for the module.

Indeed, a module and/or a program of executable code may be a singleinstruction, or many instructions, and may even be distributed overseveral different code segments, among different programs, and acrossseveral memory devices. Similarly, operational data may be identifiedand illustrated herein within modules, and may be embodied in anysuitable form and organized within any suitable type of data structure.The operational data may be collected as a single data set, or may bedistributed over different locations including over different storagedevices, and may exist, at least partially, merely as electronic signalson a system or network.

The various system components and/or modules discussed herein mayinclude one or more of the following: a host server or other computingsystems including a processor for processing digital data; a memorycoupled to said processor for storing digital data; an input digitizercoupled to the processor for inputting digital data; an applicationprogram stored in said memory and accessible by said processor fordirecting processing of digital data by said processor; a display devicecoupled to the processor and memory for displaying information derivedfrom digital data processed by said processor; and a plurality ofdatabases. As those skilled in the art will appreciate, any computersdiscussed herein may include an operating system (e.g., Windows Vista,NT, 95/98/2000, OS2; UNIX; Linux; Solaris; MacOS; and etc.) as well asvarious conventional support software and drivers typically associatedwith computers. The computers may be in a home or business environmentwith access to a network. In an exemplary embodiment, access is throughthe Internet through a commercially-available web-browser softwarepackage.

The present invention may be described herein in terms of functionalblock components, screen shots, user interaction, optional selections,various processing steps, and the like. Each of such described hereinmay be one or more modules in exemplary embodiments of the invention. Itshould be appreciated that such functional blocks may be realized by anynumber of hardware and/or software components configured to perform thespecified functions. For example, the present invention may employvarious integrated circuit components, e.g., memory elements, processingelements, logic elements, look-up tables, and the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. Similarly, the softwareelements of the present invention may be implemented with anyprogramming or scripting language such as C, C++, Java, COBOL,assembler, PERL, Visual Basic, SQL Stored Procedures, AJAX, extensiblemarkup language (XML), with the various algorithms being implementedwith any combination of data structures, objects, processes, routines orother programming elements. Further, it should be noted that the presentinvention may employ any number of conventional techniques for datatransmission, signaling, data processing, network control, and the like.Still further, the invention may detect or prevent security issues witha client-side scripting language, such as JavaScript, VBScript or thelike.

Additionally, many of the functional units and/or modules herein aredescribed as being “in communication” with other functional units and/ormodules. Being “in communication” refers to any manner and/or way inwhich functional units and/or modules, such as, but not limited to,computers, laptop computers, PDAs, modules, and other types of hardwareand/or software, may be in communication with each other. Somenon-limiting examples include communicating, sending, and/or receivingdata and metadata via: a network, a wireless network, software,instructions, circuitry, phone lines, internet lines, satellite signals,electric signals, electrical and magnetic fields and/or pulses, and/orso forth.

As used herein, the term “network” may include any electroniccommunications means which incorporates both hardware and softwarecomponents of such. Communication among the parties in accordance withthe present invention may be accomplished through any suitablecommunication channels, such as, for example, a telephone network, anextranet, an intranet, Internet, point of interaction device (point ofsale device, personal digital assistant, cellular phone, kiosk, etc.),online communications, off-line communications, wireless communications,transponder communications, local area network (LAN), wide area network(WAN), networked or linked devices and/or the like. Moreover, althoughthe invention may be implemented with TCP/IP communications protocols,the invention may also be implemented using IPX, Appletalk, IP-6,NetBIOS, OSI or any number of existing or future protocols. If thenetwork is in the nature of a public network, such as the Internet, itmay be advantageous to presume the network to be insecure and open toeavesdroppers. Specific information related to the protocols, standards,and application software utilized in connection with the Internet isgenerally known to those skilled in the art and, as such, need not bedetailed herein. See, for example, DILIP NAIK, INTERNET STANDARDS ANDPROTOCOLS (1998); JAVA 2 COMPLETE, various authors, (Sybex 1999);DEBORAH RAY AND ERIC RAY, MASTERING HTML 4.0 (1997); and LOSHIN, TCP/IPCLEARLY EXPLAINED (1997), the contents of which are hereby incorporatedby reference.

Reference throughout this specification to an “embodiment,” an “example”or similar language means that a particular feature, structure,characteristic, or combinations thereof described in connection with theembodiment is included in at least one embodiment of the presentinvention. Thus, appearances of the phrases an “embodiment,” an“example,” and similar language throughout this specification may, butdo not necessarily, all refer to the same embodiment, to differentembodiments, or to one or more of the figures. Additionally, referenceto the wording “embodiment,” “example” or the like, for two or morefeatures, elements, etc. does not mean that the features are necessarilyrelated, dissimilar, the same, etc.

Each statement of an embodiment, or example, is to be consideredindependent of any other statement of an embodiment despite any use ofsimilar or identical language characterizing each embodiment. Therefore,where one embodiment is identified as “another embodiment,” theidentified embodiment is independent of any other embodimentscharacterized by the language “another embodiment.” The features,functions, and the like described herein are considered to be able to becombined in whole or in part one with another as the claims and/or artmay direct, either directly or indirectly, implicitly or explicitly.

As used herein, “comprising,” “including,” “containing,” “is,” “are,”“characterized by,” and grammatical equivalents thereof are inclusive oropen-ended terms that do not exclude additional unrecited elements ormethod steps. “Comprising” is to be interpreted as including the morerestrictive terms “consisting of” and “consisting essentially of.”

It remains difficult to solve a Calculus problem by employing Algebra.These math tools are designed to deal with different types of problems:dynamic vs. static. Note, whenever we apply the incorrect tool to solvea problem, we inevitably settle for suboptimal solutions. Currently,hardware and software development confines solutions within a narrowrange of functionality, usually within a static, and thereforedeterministic range. Like Algebra, these implementations are very goodfor solving some problems. However, they remain inadequate when asked toaddress problems that are better answered by dynamic solutions. Beforewe can focus on dynamic solutions, we generally must first review thecurrent mathematics confining us to static solutions.

Uncertainty Function

The principle of “certainty” dominates the field of Mathematics; bycertainty, We mean that traditional mathematics provides functions whereinput values (domain values) are used to mechanically compute a certainoutput (range value). Thus far, due to the mechanical nature ofcomputation, this process has generally always resulted in adeterministic (range) value given any domain value. This truth ofdeterministic functions has held for hundreds of years.

The effects of certainty can be recognized as a major cryptographicalflaw, especially when it comes to random number generation. Many peoplehave proposed solutions where this certainty is reduced by infusing someamount of “entropy” (noise) to break up the normal certainty of functioncomputation. These solutions are major improvements, but they still fallshort in escaping the trap of mathematical certainty.

As students of Algebra, we are all taught, tested, graded and promotedby the sacred preservation of the equal sign. Each subsequent math classcontinues to reiterate this point. In order to keep both sides equal, wegenerally must manipulate the left side of the equation in the samemanner that we manipulate the right side of the equation.

After centuries of using functions, we only found deterministicfunctions. Thus we assume that only deterministic functions exist. Thislong history and our common math training prevent us from recognizingthe possibility of nondeterministic functions. There are three possiblerelationship mappings between domain and range: one-to-one(domain-to-range mapping for traditional functions), many-to-one (manydifferent domains mapping to the same range as found in Hash Functions),and the missing relationship for domain-to-range mapping—one-to-many.

So, why would the missing one-to-many domain-to-range mapping beimportant? While a traditional function is deterministic and thereforeinvertible, this new function class is nondeterministic andnoninvertible. Unlike traditional functions that can only producecertainty from a domain value, this new function class provides us with“uncertain” range values. Hence, we name each member of this newfunction family as an Uncertainty Function.

Let us demonstrate why no one can invert an uncertainty function. Wehave seen the graphical plot of many traditional functions. Thesetraditional plots prove that any range value can be mapped back to itsdomain value. In contrast, each uncertainty function domain value mapsto all uncertainty function range values. The graphical plot of onedomain value is a vertical line. Therefore, the graphical plot of allvalid domain values shows that the plot of the uncertainty function iscompletely “black”. This black graphical plot means that range valuesare independent of domain values. Thus, no one can find the uniquedomain value that produced any given range value. The UncertaintyFunction is the basis of the mathematics of uncertainty. This functionclass spawns an idea that “uncertainty” can be expanded from data intonondeterministic chaotic actions. These chaotic actions define the meansto create dynamic custom solutions that may be unique.

So, why talk about Algebra, domain vs. range relationships, andgraphical plots? Surprisingly, all current digital products already usethe Uncertainty Function. Yet none of these product designers have seenthe mathematical implications. We know this function by its more commonuse as simple RAM (Random Access Memory). The read of a memory cellwithin a buffer filled with random data produces a nondeterministicvalue. Each valid memory address maps to a memory cell that can containany possible range value. The graphical plot of RAM (the uncertaintyfunction) is black. One cannot predict the memory address (domain value)given a memory (range) value. These are profound enhancements over themathematics of certainty.

The memory buffer used within the Uncertainty Function is called the“pool of uncertainty.” Each read from this pool produces anondeterministic value called Uncertain Data. Uncertain Data remains“uncertain” if and only if no one ever sees its true value. Therefore,releasing values from the pool of uncertainty presents us with aparadox—how do we read from the data pool without exposing the contentsof the pool? This issue defines the Data Paradox.

While the Uncertainty Function is necessary, by itself, it is notsufficient to deal with the data paradox. The first functionallycomplete technology after the Uncertainty Function is the creation of anondeterministic Uncertainty Random Number Generator (URNG). Twoadditional data paradox technologies are required to protect theintegrity of the pool of uncertainty.

The Ironic Solution to the Data Paradox . . .

The attributes of uncertain data create the data paradox, but these sameattributes facilitate an ironic solution: Randomly selecting twouncertain data values from the pool and adding them together results ina new uncertain value. This new value is “decoupled” from the values ofits parents, as a plurality of different sets of parents can result inthat same value. Therefore, the attribute of “uncertainty” has carriedforward to the next generation of values. So, while one generally mustgenerally never reveal first generation uncertain data, one can releasesubsequent generations that have been processed from it, and thus, thedata paradox has been resolved. So long as randomly selected uncertainvalues are processed with most binary or higher operators, the resultsare nondeterministic. In this way, we arrive at data morphing datainstead of functions processing data. We have, in effect . . . escapedfrom mathematical certainty.

The process where we hide or decouple the first generation of uncertaindata from subsequent generations is called the Decoupling Process.Earlier in this document, the first instance of the decoupling processhas been applied to Uncertainty Function range values. To complete thetask of protecting the integrity of the pool of uncertainty, wegenerally must also decouple domain (memory address values) as well asrange values.

While the first generation of uncertain data has a fixed size,subsequent generations can be of arbitrary size. Metaphoricallyspeaking, this “pool” can be amplified into a lake, a bay, or even anocean of uncertainty depending on how much processing one chooses toinvest.

Static functions or processes generally always lead to deterministic(predictable) behavior. To overcome this behavior, we reach for the onlypure nondeterministic means we have—uncertain data. We generally mustleverage this means in order to cleanse deterministic behavior frominternal (URNG) functions, processes, and data. This multi-leveledcleansing effort results in a nondeterministic random number generatorthat is no longer limited to the size of the pool of uncertainty.Specifically, we decouple both addresses and data through the use ofuncertain data within the context of a random edit process.

Outline of URNG

The outline of each URNG has three steps. When the URNG starts, we haveonly two initial ingredients: a pristine, nondeterministic pool ofuncertainty and a collection of obscenely deterministic Pseudo-RandomNumber Generators (PRNGs). From these ingredients, we generally mustconstruct intermediate tools for internal use. The first tool is theuncertain stream. This stream is created when any good PRNG is appliedas an address generator to read from the pool of uncertainty, therebygiving us a raw, paradox unsafe, random stream of uncertain values.

The next intermediate tools utilize the uncertain stream. Using otherindependent PRNGs and the uncertain stream as inputs to a random editprocess, we obtain nondeterministic data pool addresses, which becomethe domain values to the uncertainty function. The act of using theserelatively “cleansed” domain values produces uncertainty function rangevalues that no longer echo evidence of a creation history.

The last step resolves the data paradox of the range values. Given tworange values via “cleansed” domain values, and one raw uncertain value,we can now complete the decoupling of the range values. In summary, therepetitious use of uncertain data has washed away some/most/all of thedeterministic behavior found within the intermediate tools. Thenondeterministic addresses have become the paradox safe domain values tothe uncertainty function. By decoupling both addresses (domain values)and data (range values) from the uncertainty function, we achieve ouressential goal of a nondeterministic random number generator.

A correctly implemented URNG has major advantages over a classic PRNG.In particular, the overall resistance to cracking is not derived fromthe complexity of functions; instead, it comes from the simplicity ofuncertain data. This naturally “private” random number generatorencourages a whole range of new technologies that enables manyapplications.

A “hardware selector” or “Mask Generator” takes bits from two inputvalues (data0, data1) to create a new value. Each “0” bit in the “mask”takes the corresponding bit from data0, while each “1” bit in the “mask”takes the corresponding bit from data1. The destructive edit valuespecified by the “mask” value produces the following result:

result=(^(˜)mask & data0)|(mask & data1); While data0 and data1 can bedeterministic, if the mask is nondeterministic (raw uncertain data),then the result is also nondeterministic (a paradox safe value) so longas the mask is generally never visible in the output.

Mask URNG

Examine the 10 line C procedure below to see a Mask URNG. This codeexample (urng_value) is the equivalent of the three-step processdescribed above. The section “C header” completes any missing details.

typedef struct { uint32_t poolsize; // allocation size in r_valuesuint32_t modulo; // prime number addressing modulo t_prng prng[NUM_STOCHASTIC_POINTS ]; // PRNG addressing functions r_value *pool; //data pool pointer } t_urng; r_value urng_value (t_urng *urng) { r_valueunsafe_1, unsafe_2; // paradox unsafe PRNG deterministic values r_valuedomain_1, domain_2; // paradox safe domain values r_value mask1, mask2,mask3; // Raw nondeterministic values from the pool // Use 3 independentPRNGs to read uncertain mask values from the pool mask1 = urng->pool[prng (1) % urng->modulo ]; mask2 = urng->pool[ prng (2) % urng->modulo]; mask3 = urng->pool[ prng (3) % urng->modulo ]; // Convertdeterministic PRNG values into a paradox safe domain values unsafe_1 =prng (4); unsafe_2 = prng (5); domain_1 = (~mask1 & unsafe_1) | (mask1 &unsafe_2 ) % urng->modulo; unsafe_1 = prng (6); unsafe_2 = prng (7);domain_2 = (~mask2 & unsafe_1) | (mask2 & unsafe_2 ) % urng->modulo; //Manufacture a nondeterministic value from the pool while hiding domainand range return (~mask3 & urng->pool[ domain_1 ]) | (mask3 &urng->pool[ domain_2 ]); };

An Introduction to Chaotic Actions . . .

Uncertain data plays multiple roles within the URNG: random data, seedvalues, mask values, function selectors, and even instructions. Onceagain, a seemingly trivial idea has a profound effect. The philosophy ofuncertainty dictates that many programming decisions are deferred untilexecution, at which point they are driven by random data. As randomnessplays an increasing role in program execution, the overall effect uponthe application is that it becomes less deterministic.

Given a collection of functions that resolve the data paradox, randomdata is used to select which functions will be executed. Groups offunctions can be selected to fill a list, when random data is used asinstructions (i.e. a function selector); the result is dynamic switchingbetween different functions. Thus, the dynamic creation of possiblefunctions to be executed becomes the Chaos Engine. Notice that, at anytime, functions within the list can be replaced or the entire list isdynamically recreated at runtime via random data. This reconstruction ofthe list should routinely occur at random intervals.

Like all random edit processes, neither the instruction nor the editstreams are visible in the output stream while both values come from thepool of uncertainty, (i.e. independent uncertain streams). Here is anexample of a simple 16 instruction Chaos Engine (see below): In thisinstance of a Chaos Engine, each “instruction” has three operands—data0,data1, and mask values. These values are processed into “Result”. As youcan see, not all instructions use the mask value.

Result = Data0 − Data1;  // standard math subtract operation Result =Data1 − Data0;  // standard math subtract operation Result = ({tildeover ( )}Mask & Data0) | (Mask & Data1); // normal mask Generator Result= (Mask & Data0) | ({tilde over ( )}Mask & Data1); // the other maskGenerator Result = ({tilde over ( )}Mask & {tilde over ( )}Data0) |(Mask & Data1); Result = ({tilde over ( )}Mask & Data0) | (Mask & {tildeover ( )}Data1); Result = {tilde over ( )}(Data0 & Data1); // bitwiseNAND between two data elements Result = {tilde over ( )}(Data0 |Data1);  // bitwise NOR between two data Result = Data0 | Data1; //bitwise OR between two data elements Result = Data0 {circumflex over( )} Data1; // bitwise XOR between two data elements Result = Data0 &Data1; // bitwise AND between two data elements Result = Data0 + Data1;// standard math add operation Result = {tilde over ( )}Data0 | Data1;// Comp Data0, bitwise OR two data elements Result = Data0 | {tilde over( )}Data1; // Comp Data1, bitwise OR two data elements Result = {tildeover ( )}Data0 {circumflex over ( )} Data1; // Comp Data0, bitwise XORtwo data elements Result = Data0 {circumflex over ( )} {tilde over( )}Data1; // Comp Data1, bitwise XOR two data elements

It is understood that the possible set of instructions is much greaterthan 16, and uncertain data was used to create this list (both the orderin the list and which instruction to use). Full-scale implementations ofthese nondeterministic chaotic actions have produced millions of uniquedata driven instructions for the URNG. Thus, the nondeterministic natureof uncertain data is translated into nondeterministic chaotic actions.This Chaos Software within the URNG becomes another example of datamorphing data.

As a general philosophy of chaos engines, any function can be invokedunder any number of common parameters. This data driven Chaos Softwaremay be used to create any number of reproducible stochastic systems. Itis possible to customize chaos engines with unique random data from thepool of uncertainty, or in other examples, a URNG. The generalapplication of Chaos Software is covered in the section on StochasticProcessing.

Simple simulations should guide the correct selection of implementationparameters. In this mask URNG, only destructive edit via uncertain maskvalues are used in the decoupling process. While functional, thislow-cost URNG may fail to be useful with small pools of uncertainty. Amuch better solution can be found with a Chaos Engine. The chaos URNGgives us massive amounts of random data from a reasonably sized pool ofuncertainty.

Chaos URNG

Check out the short C procedures below to see a Chaos URNG. The section“C header” completes any missing details.

typedef struct { r_value instruction; // random value holdinginstructions r_value PC; // current instruction counter within aboveinstruction edit_process operation[ MAX_REPS ]; // table of chaosoperations (Random Edit Processes) } t_chaos; typedef struct { uint32_tpoolsize; // allocation size in r_values uint32_t modulo; // primenumber addressing modulo t_prng prng[ NUM_STOCHASTIC_POINTS ]; // PRNGaddressing functions t_chaos cpuAdr0; // address chaos engine t_chaoscpuAdr1; // address chaos engine t_chaos cpudata; // data chaos enginer_value *pool; // data pool pointer } t_curng; r_value get_instruction(t_curng *urng, t_chaos *cpu) { r_value instruction, instr; instruction= cpu->instruction;  // local copy of instruction block if (cpu->PC >=INSTRUCTIONS_PER_WORD) { // need new block? instruction = urng->pool[prng(0) % urng->modulo ];  // get next instruction block cpu->PC  =0;  // reset to start of block } instr  = instruction &INSTRUCTION_MASK;  // slice off instruction from word cpu->instruction =instruction >> INSTRUCTION_SHIFT;  // move to next instructioncpu->PC++; return instr; }; r_value urng_value (t_curng *urng) { r_valueunsafe_1, unsafe_2;  // paradox unsafe PRNG deterministic values r_valuedomain_1, domain_2;  // paradox safe domain values r_value mask1, mask2,mask3; // Raw nondeterministic values from the pool r_value instr; //chaos instruction // Use 3 independent PRNGs to read uncertain maskvalues from the pool mask1 = urng->pool[ prng (1) % urng->modulo ];mask2 = urng->pool[ prng (2) % urng->modulo ]; mask3 = urng->pool[ prng(3) % urng->modulo ]; // // Decouple paradox unsafe (deterministic)values with address chaos engines // to create two paradox safe(nondeterministic) domain values. // unsafe_1 = prng (4); unsafe_2 =prng (5); instr  = get_instruction( urng, &urng->cpuAdr0 ); domain_1 =(*urng->cpuAdr0.operation[instr]) (unsafe_1,unsafe_2,mask1) %urng- >modulo; unsafe_1 = prng (6); unsafe_2 = prng (7); instr  =get_instruction( urng, &urng->cpuAdr1 ); domain_2 =(*urng->cpuAdr1.operation[instr]) (unsafe_1,unsafe_2,mask2) %urng- >modulo; // // Using data chaos engines to decouple range valuesfrom uncertainty function // instr   = get_instruction( urng,&urng->cpudata ); return(*urng->cpudata.operation[instr])(urng->pool[domain_1],urng->pool[domain_2], mask3); };

Today, there are only a few thousand known function-driven RNGs.Switching from function-driven to data-driven RNGs means trillions ofunique RNGs that can pass the National Institute of Standards andTechnology NIST (800-22), test suite for randomness. While one can passthe NIST tests with scores of 8/10, thus far, each URNG with 10/10 datapools receives almost all 10/10 scores.

TABLE 1 Comparison of Random Number Generators Classic PRNG Entropy PRNGURNG Pseudo Random Pseudo Random + Full Entropy Entropy Function DrivenFunction Driven Data Driven with data updates No Cracking ResistanceBetter Cracking Best Cracking Resistance Resistance Not valid for CryptoCurrently used for Best for Crypto Crypto Single Static Random Multiplerandom Unlimited random Stream streams/based on streams (same Entropypool)* No Sub-streams No Sub-streams Randomly Addressable Sub-streams*Static Implementations Generator not Pool shared between normally sharedusers* Not used for Crypto Used to create Crypto Public seed (timestampKeys or pool address) NA Requires Key Supports Private Exchange KeylessEncryption NA Unknown Crypto life Unique Data remains span ageless FixedImplementation Fixed Implementation Unique Date gives UniqueImplementation Seed -> start of single Seed + entropy starts Dynamicseed from stream stream timestamp *The addressability of the UncertaintyFunction means that pool addresses can be manipulated without affectingthe pool of uncertainty. Therefore, many different addressing models maybe applied to the same pool.

A one-time data exchange may now possibly replace any or allcryptographic key exchanges with a common URNG implementation. The dataexchange can effectively exchange random number generators. Thus, uniqueon-demand key generation replaces any process that formerly required keyexchanges. There are an unbounded number of ways to exchange data, fromphysical exchange of media to any form of wireless transfers. Whateverthe means, each party that holds the same data also holds the means tocreate the same random streams.

The use of dispersed, identical random number generators, which do notrequire any active connection, infrastructure, or additionalauthentication, represents a major simplification of many protocols.Furthermore, when applied to stochastic processing, the effectrepresents an exchange of custom applications/solutions.

Given an URNG implementation with isolated nonvolatile storage, aone-time load of random data provides an effective hardwareencapsulation. This permanently protected implementation can providerandom streams for use in any number of applications.

A 128 Kbyte memory buffer holds 2¹⁰⁴⁸⁵⁷⁶ unique values, which representsmuch more than 10^(300,000) values. While not all of these values becomerandom enough to pass the statistical validation process, a large numberof them qualify. While technology implementations come and go, goodrandom data remains “ageless”. So long as the pool data remains unknown,it is unlikely that any properly implemented URNG will be cracked byanalyses of the random output stream. The random number generatorresists cracking solely based on data, rather than compleximplementation.

It's about Time . . .

Domain values applied to the uncertainty function represent fine grainaddressing into the pool of uncertainty, while timestamps representlarger discontinuous jumps between different streams of uncertainvalues. We tend to think about time relative to a human scale of events.Time intervals much smaller or larger than we normally deal with remainless meaningful to us, such as microseconds or millennia. In spite ofour limitations of perception, time becomes vital throughout uncertaintytechnologies. The properties of time are tapped in many applications. Itis helpful to review these time properties so that we can understandtheir role in terms of the principles of uncertainty. A timestampgenerally always signifies a scalar value relative to some zero point.Analog/hardware clocks present a “beat count” of some kind. In software,the timestamp increment may not generally always map into a real world“beat count”, such as in the case of U.S. Pat. No. 5,526,515, which isincorporated by reference herein for its supporting teachings. While thedifference between timestamps can be computed to any value, the basicmodel of time generally always moves in a “monotonically” increasingmanner.

An increasing sequence number (as found in many digital protocols)exhibits a common, but normally unrecognized form of a timestamp.Although these sequential values do not map into a real-world view oftime, they can legitimately measure time moving forward. This representsone of the forms of time utilized throughout uncertainty technologies.

Memory addressing can be upgraded by blending timestamps into addressingcomputations, thereby recasting the base uncertainty function into onedriven as a function of time. As time continues to be monotonicallyincreasing with discontinuous jumps, the same time-based random streamshould not appear for any other value of time.

If one combines evolving time and a source file, then one obtains aunique random sequence for each instance of the URNG. This means thatrandom sequences remain completely “chaotic” with respect to itscontinuous tapping of the same source file. The continuous creation ofunique pools of uncertainty blocks/thwarts any formal analyses of randomsequences.

An uncertain time model is used to reproducibly transform any timestampinto a URNG seed value(s). As an open-ended architectural concept, thereare an unlimited number of possible time models. The timestamp may ormay not be mapped into a more familiar measure of time. These timemodels become part of a source file. Each time a source file is created,an uncertain time model is also created via uncertain random values. Anuncertain zero point in time is selected. Any input timestamp generallyrepresents an “unknowable” offset (delta) from the uncertain zero pointin time. The difference between the zero point in time and the timestampcan be expressed through (any arbitrary) units like days, hours,minutes, seconds, milliseconds, etc. Within the time model, we selectuncertain scaling factors for each supported unit of time. We thencompute required URNG seed values by scaling each supported time unit,and subsequently summing them into values that become the seed(s). Inthis way, via an uncertain time model, a public timestamp can be used todefine private seed values. Thus, anyone sharing the same time model(within a source file) can also create the same random stream. Theseseed values represent two starting indexes into the pool of uncertainty,which become the values found in the Data Congruential Generator(described below). From this first addressing function, all otherinitializations' values are read from the pool of uncertainty.

For example:

Seed0=(delta_days*dayscale)+(delta_milliseconds*millisecondscale);Seed1=(delta_hours*hourscale)+(delta_minutes*minutescale)+(delta_sec*secondscale);

Stochastic Processing

Dynamic applications simplify many solutions. The methodologies ofStochastic Processing give us an open-ended architectural means tocreate dynamic hardware or software applications. In many cases, thedeployments of these dynamic implementations redefine many current(preferred) solutions. There are additional classes of problems that canonly be effectively solved by dynamic applications. The exampleembodiment for Dynamic Digital Protocols is a class of problem requiringa dynamic application.

The current software development process has stabilized into awell-understood model of handcrafted code units, somehow joined tocreate applications. The effective use of randomness has not progressedwith the rest of software development. The current deployment of flawedrandomness technology fails to exploit its true potential. Currentapplications of randomness remain primarily limited to reducingrepetitious behaviors in gaming (gambling), video/computer games, andsimulations.

A more robust application of randomness can support the runtimeaugmentation of applications by dynamically creating updates. Thedynamic execution of updates morphs a base application into a customapplication, which may be unique. This more expansive deployment ofrandomness is called Stochastic Processing.

Design Time Vs. Runtime Binding

In the software object paradigm, the binding (making connections)between classes is completed before runtime execution. The tool setcompletes this static binding task to improve programmer productivity.However, the downside of static binding is clear, as only a limitednumber of connections are ever made. Stochastic processing makes manymore connections available at runtime instead of design time.

As the name implies, stochastic processing relates to random processing.The use of random technology in most current applications is limited toa few common tasks, such as dice (a probability function), or as a “wildcard” value (any value within a supported range). The principles ofstochastic processing make a wider range of new processing optionspossible. When used properly, these principles will solve manypreviously unresolved technology problems. The examples given here arefor teaching the concepts, and only present a sample of the value ofstochastic processing. While these ideas are simple to understand, thereader will have to think (or rethink) about how best to use them intheir applications. As hardware designers and software developers cometo understand these elements, they will be surprised by thetransformative nature of this new technology.

For effective stochastic processing, each decision that can migrate fromthe design phase to the execution phase increases the uncertainty. Forbarely any cost, some functional parameters can become data driven, thusincreasing the algorithmic complexity of the implementation. Within thesame data driven cost structure, the selection and configuration ofprocessing elements can “explode” the overall algorithmic complexity.The on-demand remixing of processing elements and/or redefiningfunctional parameters further increases the uncertainty. The net resultof a design that was limited to a single solution, now creates dynamiccustom solutions that are much more likely to be nondeterministic.

The Principles Of Uncertainty

The principles of uncertainty represent an unusual convergence of ideasacross mathematics, computer science, and electrical engineering . . .producing dynamic, custom hardware/software implementations. Outside ofsampling natural “noise”, the process of random number generation hasnot had a robust functionally complete solution. Given the mechanicalnature of computation, traditional “function-driven” solutions cannotcreate a valid representation of randomness. While function-drivensolutions remain fatally flawed, data-driven solutions can givepractical representations of randomness. The “unknowable” datatransforms a common implementation into unique random streams.

Conventional wisdom holds that hardware/software solutions arestatically defined during development, so these traditional solutionsinevitably lead to a “one size fits all” mentality. Worse yet, theseimplementations are limited to a single solution for any given problem.The effect of an isolated solution means that they often becomedeterministic. Deterministic behavior is the antithesis of randomness.This flawed (deterministic) behavior extends across many applicationsthat are better solved with dynamic, rather than static, solutions.

Currently, software (and hardware) applications can be defined as a“joined” collection of components. Instead of limiting the set ofcomponents to be “just enough” to create a single solution, we increasethe pool of “functionally equivalent” components so that one couldcreate many solutions. Next, we use random data to select, duringexecution, which set of components will be used to create this instanceof the application. Thus, from a common implementation, driven by randomdata, we have created an uncertain custom solution that may benondeterministic.

The Uncertainty Random Number Generator (URNG) is a dynamic solution ineither hardware or software. Within the current URNG implementation,there are over one hundred components that become joined (and remixed)as needed during execution to create a nondeterministic random numbergenerator. Even better, since this is a data driven solution, each useof the URNG can be driven by a dynamically created pool of uncertaintythat may be used once and then discarded. With this degree ofrandomness, these URNGs can be the means to drive future dynamicapplications.

“Functional Equivalency” is Solely Based on Application Requirements

Within the methodologies of Stochastic Processing, the meaning of“functionally equivalent” is much more sensitive than the typical casefound in the object paradigm. Typically, the software paradigm leadsdevelopers to ignore most implementation side effects in order to raiseproductivity. Sometimes, these side effects matter and ignoring themundermines the developer's goals.

For example, let us note the case of random number generators. There arethousands of them and most have the same properties. Most of thesegenerators remain relatively fast but not cryptographically secure,while a few are secure but slow. So, if one sorts between secure andunsecure generators, are they roughly equivalent within each category?No. When we acquire multiple random numbers from the same unsecuregenerator, we find that the values are highly correlated to the extentthat one can predict the next value. Hence, it is not “random”. Thoughsecure generators appear less correlated, they remain slower and rarelysharable.

Current unsecure random number generators are assumed to be functionallyequivalent, but each of them are ineffective at producing random valuesbecause there is a single random stream for each generator. The onlyfunctional equivalence of these unsecure random number generators restswith their flaws, yet application requirements for randomness stillremain. Therefore, we generally must be careful when defining functionalequivalence. Failure to define functional equivalence correctly mayresult in an application design as flawed as existing unsecure randomnumber generators.

Some background information on current random number generators isrequired to explain these new methodologies of Stochastic Processing.Any given “functionally equivalent” processing element may have unusualside effects that are useful. Within the first teaching example,multiple common (but flawed) random number generators are used toreplace the sole default generator found in software libraries.Additional teaching examples show a richer illustration of functionallyequivalent processing elements.

Stochastic “Scaffolding Points”

The first teaching example has seven calls to different “random numbergenerators” to create memory addresses. Any method that provides goodrandom memory addresses could therefore replace these classic PRNGs, asthey would be functionally equivalent. To overcome the basic flaws of“single stream” PRNGs, each current call is routed to a different PRNG.So, each one of them returns values from a different random sequence,which is much better than seven sequential values from the samesequence. While multi-PRNGs solution is a major improvement, the bestsolution for all other dynamic applications is to use an URNG to providenon-correlated, nondeterministic values.

Failure to deploy either of the above solutions produces poor results.Applying classic PRNGs to select processing elements remains as flawedas the generators, only producing deterministic selections. Instead,using the Uncertainty Function or the URNG to select processing elementsproduces nondeterministic selections. Any point in a hardware orsoftware application that can accept a functionally equivalentprocessing element is called a stochastic “scaffolding point”.

Collections of Processing Elements Abstraction

Application-specific collections of processing elements are created toprovide many options. Each time we define such a collection ofprocessing elements, we generally must size the collection and definethe required “quality” of each element. Often, though an unboundednumber of processing elements may exist from which we can select, mostapplications only require the collections to have many times what theynormally deploy. Clearly, some applications will replace common choiceswith their own custom collection.

The term “collection of processing elements” is an abstraction that canbe implemented in different ways. For example, in hardware, an LFSR(Linear Feedback Shift Register—classic hardware random bit-valuegenerator) is seeded with a value to create a random bit stream.However, if a programmable LFSR is used, it can be reconfigured togenerate many different random bit streams. In this way, theconfiguration options within the programmable LFSR define the possiblecollection, thereby providing choice. These configuration options createa virtual collection.

The same virtual collection idea can be applied through options insoftware. For example, a set of optional uncertain parameters can bedefined to further process operands of the URNG's Random Edit Process.In this case, the pre-processing of operands can include bitwise rotateleft/right (with bit count), bitwise NOT, reverse bit order, etc. Thesame sequence of options can be applied to the result. The deployment ofthese uncertain parameters (options) “explodes” the size of the normalcollection to create a massive virtual collection.

As for the quality of each processing element, this also becomes anapplication-specific design choice. In the case of the URNG itself, thefirst patent dealt with the use of inexpensive, yet deterministic PRNGs.A better quality replacement for creating uncertainty function domainvalues can be found in the Data Congruential Generator (defined later).However, we can continue with the valid teaching examples assumingcommon PRNGs.

The creation of the collection of processing elements was to facilitateruntime selection. We use random data to select which element(s) to use.FIG. 1 is a flowchart depicting the selection process. Note the casewhere selection is required.

FIG. 1 is a flowchart depicting an embodiment of selecting processingelements for this instance of stochastic module/process 100 in which theinitial condition is set to not “Done=0” 110. The loop until “Done” 120continues with obtaining an Uncertain Value “X” that is used to select apossible Processing Element from the PE_table 130. This possibleprocessing element is tested (see FIG. 2) to see if it is already in use140. If the Processing Element is already “in use”, then the loop startsover with the next Uncertain Value 120. However, if the ProcessingElement is “not in use”, then a test is made looking for the next “free”(available) entry 150, where the Processing Element is assigned to thefree entry 160, and the loop starts over. If the last entry has beenfilled 170, then the terminating condition “Done=1” is set 180 and theloop starts over. The embodiment of selecting processing elementsterminates when “Done” is true.

FIG. 2 is a flowchart depicting an embodiment of testing if the givenProcessing Element is “In Use” 200, in which the initial condition isset to not “Found=0” 210. The (for) loop starts with the first indexuntil the entire table is indexed 220 and then exits the loop tocontinue by returning the “Found” status 250. The table entry indexed istested against the given element 230, and if “found” then “Found=1” isset 240, and the (for) loop continues.

FIG. 3 illustrates a system of stochastic processing of informationaccording to one embodiment of the invention. There is shown anarchitect module coupled to each of a data pool module, a set of dataprocessing modules, a data feed module, and a structure memory module.The illustrated data pool module is functionally coupled to each of acommon data pool processing module and a communication protocolinterface. The illustrated system is utilized to process an informationstream (TCP/IP packets, telephone data, wireless communications data,private protocol communications, media files, data files, databases, andetc.) in a manner that transforms the information stream. This isgenerally done to prevent hacking of the information stream.Accordingly, the system may be used to enhance privacy, validatecommunications, verify authorship/source of communications, and the likeand combinations thereof.

The illustrated architect module is configured to manage and controlstochastic processing of data and may include a processor and/or may beassociated with a processor, processor module, processing device/systemor the like. It may also include one or more scripts for accomplishingthe same and such scripts may be replaceable and/or associated withspecific applications of the system. As a non-limiting example, theremay be a script configured to provide optimal function for generation ofdigital signatures and/or certificates. An architect module may includea plurality of scaffolding points that may operate to receive othermodules, especially data processing modules. Such scaffolding points maybe predetermined and/or may be generated during run-time. Such pointsmay interact with each other and may be sources of data for each other.Accordingly, the complexity of data processing may be predeterminedand/or may be generated during run-time. An architecture module mayinclude instructions on limitations of the scaffolding points includingbut not limited to maximum/minimum levels, points, connections, sources,redundancy of data processing elements, and the like and combinationsthereof. The illustrated architect module may use random values toselect between a plurality of functionally equivalent data processingmodules during run-time. The architect module may use random values toselect run-time durations for use of one or more of the plurality offunctionally equivalent data processing modules during run-time. Suchdurations may be in actual time, clock cycles, data chunks processed,numbers of times or portions thereof of cycles made through repeatingcycles of the data processing module, and the like and combinationsthereof. Non-limiting examples of an architect module may be a controlmodule as described in U.S. Pat. No. 5,430,836, issued to Wolf et al.;or a control module described in U.S. Pat. No. 6,243,635, issued to Swanet al. which are incorporated for their supporting teachings herein. Anarchitect module may include but is not limited to a processor, a statemachine, a script, a decision tree, and the like.

The illustrated architect module includes a run-time modification moduleconfigured to randomly alter a stochastic architecture during run-time.The run-time modification module may be seeded from thenon-deterministic data pool module, thereby enhancing the randomnesscharacteristics of the scaffolding over PRNG sources. The run-timemodule includes instructions for making alterations to the number ofscaffolding points, the interconnections there between, the dataprocessing modules used therewith, and/or the durations between suchchanges, and the like. Such a module may also track progress through adata processing task and as such may receive and act on informationassociated with the context of the data stream being processed(remaining amount/time of data to process, presence of repeatingstrings, number of communication cycles between respective parties, andthe like and combinations thereof) and may alter operation of therun-time module during run-time in response to changes in suchinformation. Non-limiting examples of a run-time modification module maybe a modification system as described in U.S. Pat. No. 6,898,788, issuedto Kosaka et al.; or a modification module as described in U.S. PatentPublication No.: 2004/0205567 by Nielsen which are incorporated fortheir supporting teachings herein.

The illustrated non-deterministic data pool module is functionallycoupled to the architect module so to be accessible by the same and/orby associated modules. It is configured to provide a stream ofnon-deterministic values that are, ideally not derived from a classicalfunction (therefore non-deterministic). In one non-limiting example ofsuch a pool, the non-deterministic data pool module includes one or morecomponents of a URNG system. Such a pool may be sized to fit aparticular desired use and/or may be used to create larger or smallerpools that may be used in the same or a similar manner. Non-limitingexamples of a non-deterministic data pool may be a uncertain randomnumber generator as described in U.S. Patent Publication No.:2010/00036900 and U.S. Patent Publication No.: 2011/0029588 both by Rosswhich are incorporated for their supporting teachings herein.

The illustrated plurality of functionally equivalent data processingmodules are functionally coupled to the architect module. In particular,each is configured to stochastically process data as called upon by thearchitect module. Generally, such data processing modules will include adefined operation used in association with a random value stream. As anon-limiting example, such a module may add, bitwise or in other“chunks,” a random value from a random value stream to a value of aninformation/data stream thereby forming a transformed data value and maydo so over and over when called upon. Accordingly, such a modulegenerally requires access to a random value generation tool, such as butnot limited to a PRNG, URNG, etc. Where maximum decryptable protectionis required, a URNG is generally selected as a source as it will operatein a manner than is non-deterministic (one-way) and is reversible forthose who have a copy of the pool used. Non-limiting examples of dataprocessing modules may be a data processing system as described in U.S.Patent Publication No.: 2010/0318851 by Learmonth; or a data processingmodule as described in U.S. Patent Publication No.: 2009/0259862 byBulusu et al., which are incorporated for their supporting teachingsherein.

The illustrated data feed module is in functional communication with thearchitect module and is configured to feed a data set desired to bestochastically processed. Such a module may include instructions and/orhardware configured to manage, access, control and otherwise providedata to the system. Such a system may include interface tools fortransforming data from its source to a form that is more usable by thesystem, such as through analog to digital or from one protocol toanother. Such a system may transmit/receive data and/or may store thesame. Non-limiting examples of a data feed module may be an in-feedmodule as described in U.S. Pat. No. 5,957,714, issued to Johnson etal.; or a feed module as described in U.S. Patent Publication No.:2010/0241417, by Bassett et al., which are incorporated for theirsupporting teachings herein.

The illustrated structure memory module is coupled to the architectmodule and is configured to provide sufficient information for anarchitect module to duplicate a predefined processing architectureand/or to record a utilized processing architecture such that it may beused later, by the same system and/or a similar/companion system. Theremay also be a memory storage device as part of or functionally coupledto the structure memory module. The illustrated structure memory moduleincludes an index module that indexes structure according to a timestructure. This is particularly advantageous because it permits timestamps of various sorts to be used as keys for seeding the URNG. Suchtime stamps are generally readily available, short, inexpensive toproduce and/or transmit and permit associate systems using the same datapools to easily reverse data transformation. Such time stamps may be inthe form(s) of actual time, clock cycles, data chunks processed, numbersof times or portions thereof of cycles made through repeating cycles ofthe data processing module, and the like and combinations thereof.Non-limiting examples of a memory storage device may include: a HPStorage Works P2000 G3 Modular Smart Array System, manufactured byHewlett-Packard Company, 3000 Hanover Street, Palo Alto, Calif., 94304,USA; a Sony Pocket Bit USB Flash Drive, manufactured by Sony Corporationof America, 550 Madison Avenue, New York, N.Y., 10022. Data storagemodules may be databases or data files, and the memory storage devicemay be hard drives or tapes. A non-limiting example of a data base isFilemaker Pro 11, manufactured by Filemaker Inc., 5261 Patrick HenryDr., Santa Clara, Calif., 95054.

The illustrated common data pool processing module is functionallycoupled to the non-deterministic data pool module and is configured tostochastically process a common non-deterministic data pool therebygenerating an application specific non-deterministic data pool for useby the non-deterministic data pool module. In this way, a user maygenerate a personal non-deterministic data pool that may be used in manydifferent settings and shared (in its transformed form) with a greatvariety of others without compromising the integrity of the common pool.In a way, the system may be used to generate an (virtually) unlimitednumber of non-deterministic data pools from a single common pool,without sharing the common pool and violating the integrity of the set.Non-limiting examples of a data pool processing module may be a systemas described in U.S. Pat. No. 5,573,244, issued to Mindes; or a systemas described in U.S. Pat. No. 5,517,556, issued to Pounds et al.

The illustrated communication protocol interface is in communicationwith the data feed module and is configured to feed a communicationprotocol map to the data feed module, receive a stochastically processedcommunication protocol map from the architect module, and/or to alter aninformation stream according to the stochastically processedcommunication protocol map. Such a communication protocol interface mayoperate to “break” a stream of information such that thosedevices/systems relying on data to meet certain minimum standards for aparticularly defined data protocol will reject, fail to use, fail to“display” or otherwise determine that the information is unusable. As anon-limiting example, some protocols will reject packets of informationthat fail to meet a parity check. A communication protocol interface mayoperate the make certain that some or all packets in an informationstream are transformed sufficiently to fail the parity check so thatthey are rejected by those who are not undoing the transformation. Suchpackets may appear to be simply bad packets by eavesdroppers.Non-limiting examples of a communication protocol interface may be acircuit as described in U.S. Patent Publication No.: 2010/0277104, byLin et al.; or an interface as described in U.S. Pat. No. 7,058,075,issued to Wong et al.; which are incorporated for their supportedteachings herein.

According to one embodiment of the invention, there is a system ofstochastic processing of information using a computing device 10. Thesystem 10 includes an architect module 12 that includes a processor 14.The architect module 12 is configured to manage and control stochasticprocessing of data. The architect module 12 includes a run-timemodification module 16 that is configured to randomly alter a stochasticarchitecture during run-time.

The system 10 includes a non-deterministic data pool module 18 isfunctionally coupled to the architect module 12 and is configured toprovide a stream of non-deterministic values that are not derived from afunction. The run-time modification module 16 is seeded from thenon-deterministic data pool module 18. The non-deterministic data poolmodule 18 includes a URNG system 20. The system 10 includes a pluralityof functionally equivalent data processing modules 22 that arefunctionally coupled to the architect module 12, and each may beconfigured to stochastically process data as called upon by thearchitect module 12. The architect module 12 is configured to use randomvalues to select between the plurality of functionally equivalent dataprocessing modules 22 during run-time. The architect module 12 isconfigured to use random values to select run-time durations for use ofone of the plurality of functionally equivalent data processing modules22 during run-time.

The system may 10 includes a data feed module 24 that is in functionalcommunication with the architect module 12 and is configured to feed adata set desired to be stochastically processed. The system 10 includesa structure memory module 26 that includes a memory storage device 28.The structure memory module 26 is coupled to the architect module 12 andis configured to provide sufficient information for the architect module12 to duplicate a predefined processing architecture and to record autilized processing architecture. The structure memory module 26includes an index module 30 that indexes structure according to a timestructure.

The system 10 includes a common data pool processing module 34 that isfunctionally coupled to the non-deterministic data pool module 18 andconfigured to stochastically process a common non-deterministic datapool thereby generating an application specific non-deterministic datapool for use by the non-deterministic data pool module. The system 10includes a communication protocol interface 32 that is in communicationwith the data feed module and is configured to feed a communicationprotocol map to the data feed module, receive a stochastically processedcommunication protocol map from the architect module, and to alter aninformation stream according to the stochastically processedcommunication protocol map.

FIGS. 4-5 illustrate a method of stochastically processing informationusing a computing device. The illustrated steps permit adata/information stream to be processed in a manner that causes one-waytransformation of the data while still permitting others who havesufficient information about the process to reverse the transformationand thereby have access to the data. Eavesdroppers and others who maygain access to the transformed data will not be able to use shortcuts tohack the data as the transformation is a one-way transformation.Further, because the process permits variation in the transformationoperations, a single pool of non-deterministic values may be utilizedalmost infinitely without substantially devaluing the pool. In summary,a non-deterministic data pool is used to seed a scaffold of randomlyselected data transformation processes that operate on an informationstream while sufficient information about the process is recorded suchthat the information may be provided to another user of the method toundo the transformation, thus enabling extremely powerful andoperationally inexpensive privacy, security, authentication, and otherbenefits. The illustrated steps are described individually below and itis understood that the illustrated order of steps is not necessarily theonly order that may be utilized in operation of the method. Further, notall steps are necessary for various applications of the method.

The step of providing a non-deterministic data pool that is verified tobe non-deterministic and is not derived from a function permits abedrock of variability to be used in the method without subjecting themethod to the weaknesses and vulnerabilities of deterministic PRNGfunctions but permitting reversibility not offered by environmentalrandomness sources. Such a pool may be formed as a URNG as describedherein. Such a pool may be provided as a stored pool of data, a streamof data, or the like or variations thereof.

The step of providing an information stream to be processed permits themethod to act upon an information stream. Such may be provided throughcommunication tools/modules of any type that provides the information ina usable form to a system/device that may be operating the method. Theinformation may be provided as a stream over a communications network(wireless, internet, intranet, bus, etc.) and/or may be provided throughaccess to a memory device and/or memory feed such as but not limited tohard drives, flash memory, ROM, RAM, optical discs, and the like andcombinations thereof.

The steps of randomly selecting a first data processing module, seedinga step of randomly selecting a data processing module from thenon-deterministic data pool, altering the information stream by use ofthe first data processing module, randomly selecting a replacement dataprocessing module replacing the first data processing module with thereplacement data processing module, stochastically processing thenon-deterministic data pool before utilization of the non-deterministicdata pool, randomly layering use of a plurality of data processingmodules such that the information stream is processed through multiplerandomized layers of data processing modules, managing randomizationsuch that each call to a source of random values goes to a differentsource than each previous call and altering the information stream byuse of the replacement data processing module collectively andindividually provide layers of structured variability within the method,thereby multiplying the usability of a single data pool and obscuringthe data pool itself. Such a random selection may be seeded by the datapool and/or by another source. Such a random selection may be performedusing another instance of this method. Such a random selection may beperformed using a processor. The data processing modules selected frommay be from a set of functionally equivalent data processing modulesthat may each be configured to alter data. Non-limiting examples ofprocesses performed by data processing modules including: subtraction,masking, NAND, NOR, OR, XOR, AND, and addition and the like andcombinations thereof.

The step of recording structure information sufficient to reproduce useof the first and replacement data processing modules permits such aone-way transformation to be reversed, thereby providing a usefulbenefit to receivers of the transformed data. Such recording may be assimple as a time stamp where corresponding systems are sufficientlyconfigured and sufficiently identical to permit a time stamp to serve asa key for unlocking the data. Such recording may be more complicated andmay include one or more scripts, data sets, and/or data processingmodules that may be transmitted/packaged with and/or in association withtransformed data.

The steps of delaying selection of all randomized data processingcharacteristics until run-time and/or determining a random duration ofuse of the first data processing module during run-time are very helpfulin strengthening the resulting transformation against attack. Such astep may include having a predefined scaffolding structure but notselecting data processing modules to operate therein until runtimeand/or not selecting seed values to be used in such systems untilrun-time.

The step of configuring an information stream according to a predefinedcommunication protocol and the first and replacement data processingmodules each sufficiently process the information stream to make theinformation stream fail to satisfy the requirements of the predefinedcommunication protocol facilitates very inexpensive (processing cost)data protection because only time changes need to be made to theresulting stream in order to make eavesdroppers reject the data ascorrupted, while recipients may only need to change a smaller percentageof the data in order to properly utilize the same.

The step of associating operation of the method with a time index suchthat operation of the method by counterparts beginning with identicaltime index positions and an identical non-deterministic data pool willprocess the information stream identically.

The step of verifying that the non-deterministic data pool passes theNIST test with a predominant 10/10 score provides a degree of securitynot otherwise found in other methods. Passing with a predominant 10/10score means that more than 85%, 90%, 95%, 99%, and/or 99.9% of suchtesting results in a 10/10 score for non-overlapping template testing,serial testing, and linear complexity testing, while scores of 2/2 or1/1 are achieved for all RandomExcursions testing. In particular, theNIST test referenced herein is the test for the Uniformity of P-Valuesand the Proportion of Passing Sequences found in the NIST SpecialPublication 800-22, A Statistical Test Suite for Random and PseudorandomNumber Generators for Cryptographic Applications which may be found athttp://csrc.nist.gov/groups/ST/toolkit/rng/documents/SP800-22b.pdf whichis incorporated by reference herein for its supporting teachings. In onenon-limiting embodiment, only a common data pool is verified/testedthusly while subsequent pools created therefrom using the method(s)described herein are not tested. This is advantageous because suchtesting is computationally expensive and because it has been observedthat pools that pass the NIST test and are then transformed by thismethod will continue to pass the NIST test without substantialdegradation in the degree to which the test is passed. This is generallyonly possible with functions that are exponentially more expensive asfurther data is produced, while the presently described method islinearly expensive (time).

According to one embodiment of the invention, there is a method ofstochastically processing information using a computing device 40. Themethod 40 includes the step of providing a non-deterministic data poolthat is verified to be non-deterministic and is not derived from afunction 42. The method 40 includes verifying that the non-deterministicdata pool passes the NIST test with a predominant 10/10 score 44. Themethod 40 includes the step of providing an information stream to beprocessed 46. The method 40 includes the step of delaying selection ofall randomized data processing characteristics until run-time 48. Themethod 40 includes randomly selecting a first data processing module,using a processor, from a set of functionally equivalent data processingmodules, each configured to alter data 50.

The method 40 includes the step of determining a random duration of useof the first data processing module during run-time 52. The set offunctionally equivalent data processing modules is selected from thegroup of data processing modules including: subtraction, masking, NAND,NOR, OR, XOR, AND, and addition. The method 40 includes seeding a stepof randomly selecting a data processing module from thenon-deterministic data pool 54.

The method of stochastically processing information using a computingdevice 40 includes the step of altering the information stream by use ofthe first data processing module 56. The method 40 includes randomlyselecting a replacement data processing module, using a processor, fromthe set of functionally equivalent data processing modules whileprocessing the information stream with the first data processing module58. The method 40 also includes replacing the first data processingmodule with the replacement data processing module 60.

The method 40 includes the step of altering the information stream byuse of the replacement data processing module 62. The information streamis configured according to a predefined communication protocol and thefirst and replacement data processing modules each may sufficientlyprocess the information stream to make the information stream fail tosatisfy the requirements of the predefined communication protocol. Themethod 40 includes randomly layering use of a plurality of dataprocessing modules such that the information stream is processed throughmultiple randomized layers of data processing modules 64.

The method 40 further includes the step of recording structureinformation sufficient to reproduce use of the first and replacementdata processing modules 66. The method 40 includes associating operationof the method with a time index such that operation of the method bycounterparts beginning with identical time index positions and anidentical non-deterministic data pool may process the information streamidentically 68. The method 40 includes the step of stochasticallyprocessing the non-deterministic data pool before utilization of thenon-deterministic data pool 70. The method 40 also includes the step ofmanaging randomization such that each call to a source of random valuesgoes to a different source than each previous call 72.

FIG. 6 illustrates a stochastic processing device configured tostochastically process information fed therein. The illustrated deviceincludes a processor respectively coupled to a non-volatile memorydevice, a data input interface module, a data output interface module,and an architect module. In operation, the device permits a user to takea data/information stream to be processed and process it (transform it)in a manner that causes one-way transformation of the data while stillpermitting others who have sufficient information about the process toreverse the transformation and thereby have access to the data.Accordingly, a single device may be used to provide enhanced privacy,security, authentication, validation, verification and the like andcombinations thereof for users of the same.

The illustrated processor may include one or more processing devicessuch as those found in common electronic devices (computers, servers,tablets, smartphones, etc.).

The illustrated non-volatile memory device may include one or morememory devices that does not lose data when unpowered. Hard drives andflash drives are non-limiting examples of such. The memory device isfunctionally coupled to the processor and includes a pool ofnon-deterministic data that is verified to have passed the NIST testwith a predominant 10/10 score.

The illustrated data input interface module is functionally coupled tothe processor and configured to receive data. The data output interfacemodule is functionally coupled to the processor and configured to senddata. Such interface modules may include data ports, USB ports, serialports, network cards, wireless transmitters/receivers and the like andcombinations thereof. Such will permit the device to communicate withother devices and/or systems.

The illustrated data processing module is functionally coupled to theprocessor and includes a plurality of functionally equivalent dataprocessing instruction sets, or a library of such functions and/or datapools. Such may include one or more instances of a URNG pool/system.

The illustrated architect module is functionally coupled to theprocessor, the data processing module and/or to the non-volatile memorydevice and is configured to manage and/or control stochastic processingof data according to seed values from the pool of non-deterministicdata. Such may be accomplished by randomly selecting data processingmodules during run-time, thereby processing data received through thedata input interface module and providing stochastically processed datato the data output interface module. Such an architect module mayinclude one or more features, structures, functions and/or the like asdescribed elsewhere herein.

According to one embodiment of the invention, there is a stochasticprocessing device 80 configured to stochastically process informationfed therein. The device 80 includes a processor 84 and a non-volatilememory device 86 that is functionally coupled to the processor 84. Thenon-volatile memory device 86 includes a pool of non-deterministic data88 that is verified to have passed the NIST test with a predominant10/10 score. The device 80 includes a data input interface module 82that is functionally coupled to the processor 84 and is configured toreceive data. The device 80 includes a data output interface module 92that is functionally coupled to the processor 84 and is configured tosend data.

The device 80 also includes a data processing module 90 that isfunctionally coupled to the processor 84 and includes a plurality offunctionally equivalent data processing instruction sets 96. The device80 includes an architect module 94 that is functionally coupled to theprocessor 84, the data processing module 90 and to the non-volatilememory device 86. The architect module 94 is configured to manage andcontrol stochastic processing of data according to seed values from thepool of non-deterministic data by randomly selecting data processingmodules during run-time, thereby processing data received through thedata input interface module and providing stochastically processed datato the data output interface module.

Uncertain Application Parameters

Since static parameters lead to deterministic results, many processingelements will require additional parameters to function correctly.Whenever possible, all of these parameters should be dynamicallyacquired from the uncertainty function or the URNG. In this way, eachadditional uncertain parameter continues to expand the scope ofuncertainty.

Control Plane Responsibilities

The concept of the “control plane” was first covered in the originalURNG patent. While the idea remains the same, the responsibilities havebeen generalized for stochastic processing. The selection/reselection ofprocessing elements, initialization or re-initialization of processingelements, and updating of stochastic scaffolding points, are allexecuted with the primary goal of dynamically replacing many/most/all ofthese values/elements at some point during runtime execution.

An application designer is responsible for defining the policies thatupdate processing elements or dynamic values. Simple tools like“countdown counters”, or as complex as “application specific events”could be defined to trigger an update. These policies become importantimplementation details.

Stochastic Development

Within the methodologies of stochastic processing, random values inhabitmany different functional roles, including selection of processingelements, programmatic control parameters, and simple data. Properlyconstructed stochastic processing scaffolding will also support the useof random values as software instructions. The simplest form of thistype of scaffolding is the URNG's Chaos Engine. The Chaos Enginerepresents a single selection from a list of choices.

Within the limitations of our development tools and/or imagination, theopen-ended application solution space remains a challenge to theprogrammer or hardware designer. The use of stochastic developmentconcepts is just as open-ended; any dynamically defined quantities ofprocessing elements, steps, passes, loops, and sequences can be created.In traditional programming, “control variables” direct the path throughthe code. Within stochastic development, many control variables aredefined/or redefined at runtime with uncertain values. The unlimitedrange of chaotic actions remains to be defined by the complexity of thisscaffolding. Once the scaffolding is set, only nondeterministic valuesare needed to exploit the chaotic actions. This newly created,unknowable runtime software completely transforms the application designspace.

Stochastic “Scaffolding Points” Example

Look at the routine below and assume the following: design time staticpoolsize, addressing modulo, and a single (library) PRNG call. Clearly,the same routine would be more deterministic. So, we add methodologiesof Stochastic Processing, with a substantial collection of PRNGfunctions from which to choose. Each of the following PRNG calls isdynamically selected with a different function and seeded via uncertaindata. In addition, the “poolsize” and “modulo” uncertain parameters aredefined when the source file is created. In this way, some of the knownflaws of a single PRNG are mitigated while the overall scope ofuncertainty is widened. The net effect is a nondeterministic, uncertainrandom number generator known as the Mask URNG.

typedef struct { uint32_t poolsize; // allocation size in r_valuesuint32_t modulo; // prime number addressing modulo t_prng prng[NUM_STOCHASTIC_POINTS ]; // PRNG addressing functions r_value * pool; //data pool pointer } t_urng; r_value urng_value (t_urng *urng) { r_valueunsafe_1, unsafe_2; // paradox unsafe PRNG deterministic values r_valuedomain_1, domain_2; // paradox safe domain values r_value mask1, mask2,mask3; // Raw nondeterministic values from the pool // Use 3 independentPRNGs to read uncertain mask values from the pool mask1 = urng->pool[prng (1) % urng->modulo ]; mask2 = urng->pool[ prng (2) % urng->modulo]; mask3 = urng->pool[ prng (3) % urng->modulo ]; // Convertdeterministic PRNG values into a paradox safe domain values unsafe_1 =prng (4); unsafe_2 = prng (5); domain_1 = (~mask1 & unsafe_1) | (mask1 &unsafe_2 ) % urng->modulo; unsafe_1 = prng (6); unsafe_2 = prng (7);domain_2 = (~mask2 & unsafe_1) | (mask2 & unsafe_2 ) % urng->modulo; //Manufacture a nondeterministic value from the pool while hiding domainand range return (~mask3 & urng->pool[ domain_1 ]) | (mask3 &urng->pool[ domain_2 ]); };

However, by adding the dynamic unknowable replacement of these PRNGswith new seed values, and creation of on-demand pools of uncertainty,the same implementation then becomes a dynamic custom solution each timeit is invoked.

Stochastic Scaffolding—Chaos Example

We now start with two collections, one for PRNG functions and anotherone for URNG Random Edit Processes (REP) functions. Using the same datadriven processes for selecting PRNGs, we also select and create a REPlist. Each random edit process is a decoupling process. While the MaskGenerator represents only one decoupling process, the Chaos URNG (seeabove code) uses uncertain values to choose among many decouplingprocesses. The overall result is substantially more uncertain randomvalues created from the same pool of uncertainty.

Effects Of Stochastic Processing

The uncertainty of stochastic processing demonstrates why this is a muchmore useful application of randomness. Without the exact pool data, noone can predict, (from knowledge of the implementation), which versionof a custom solution will be invoked. Current (static) implementationsin hardware and software conform to mathematical certainty. These staticsolutions will give way to dynamic solutions. These technologies ofuncertainty will free applications to become chaotic, less expensive andmuch better solutions.

Dynamic Digital Protocols

Data structures and actions define current digital protocols. Developersapply painstaking efforts to perfect implementations of these protocols.This near perfection of implementation means that most current digitalprotocols are extremely fragile. How do we protect fragile datastructures within digital protocols? Only by deliberately breaking themdo we gain the means to protect them . . . this irony defines the DataStructure Protection Paradox.

To utilize this paradox, with the aid of stochastic processing, we usedata structure fragility as a tool—we create a dynamic means to uniquely“break” the perfection, and then uniquely “restore” the perfection ofthe same data structure. We can see the value of this fragility with asimple contrast. While a canon can kill a housefly, so can a flyswatter. Both complete the task, yet one of them remains completelyinappropriate for the scale of the problem. Encryption can protect adigital protocol, yet it remains as inappropriate as a canon is to killa housefly as each appears to represent massive overkill.

Given this understanding about sensitivity to disruption, we should lookcloser at the internal data structures of these protocols. Today, alldigital protocols are defined to be static with respect to datastructures. Instead of encryption to gain protection from hacking, weshould apply “dynamic evolution” to transform these static protocol datastructures into unknowable “moving targets”. The ongoing, lightweightrandom evolution allows people to still read (with effort) the datastructures, but prevents programmers from predicting the nextevolutionary change. Thus, we gain the hacking protection of encryptionfor barely any cost.

To demonstrate how this works, we start with a common example of adigital protocol—computer instructions. Changing just a few bits caneasily break the underlying software defined by these instructions. Innearly every case, the change of just one bit per instruction results inthe destruction of the software program. Therefore, the breaking and therestoration of software can be an inexpensive lightweight process.

Any given CPU processor defines public data structures for itsinstruction. A custom, inexpensive, and dynamic means to break and thenrestore software instructions can transform these public protocols intoprivate protocols. This transformation would make a useful tool toimplement a Digital Rights Management (DRM) system for software, books,media, etc.

Basic economics drives the demand for custom solutions. When peopleshare a common DRM system, they also share the same risks of hacking.The cracking of the first DVD copy protection exposed the risks of usingcommon protection. Once the first DVD copy protection was cracked, thenall protected DVDs were at risk. Thus, the sharing of common protectioneffectively invites large economic payoffs to defeat them. In contrast,if all media were guarded by custom protection, then the successfulcracking of one piece of protected media would not risk any otherprotected media. This poor return on effort would foil most cracking forprofit.

Since shared data protection invites larger economic payoffs, it alsogenerally requires stronger protection (Blu-ray for example). However,custom protection justifies simpler implementations. The deployment ofdynamic digital protocols resets these basic rules of economics. Afterillustrating why the fragility of protocols is helpful, as well as theeconomics, we now discuss the details of implementation.

Common Custom Environment

There are two parties in most digital protocols—the producer (source orinitiator) and the consumer who processes the protocol. To be successfulwith dynamic digital protocols, these two parties generally must startwith the same custom environment, which include a common source file,timestamp, URNG implementation, and stochastic processingapplication(s). In this way, both parties know that they have the samerandom data stream(s) and application(s) to process them into compatibledynamic digital protocols. So long as the dynamic digital protocol“producer” and “consumer” are synchronized, they can continue toexchange data structures without concern that they may be hacked.Without exactly the same environment, no one else can create theexpected data structure that will be accepted by the protocol consumer.

Innovations Around the Data, Address, and Control Planes of Randomness

Each of these “planes” represents an independent opportunity forinnovation. One can change the addressing into the data pool withoutaffecting the data. Likewise, fixing (freezing the state) on one planestill allows developers to innovate in the other planes. Theseopportunities for innovation flow from the properties of the UncertaintyFunction. Therefore, these same opportunities are available withinstochastic processing (data/address/control planes).

Stochastic processing generally requires the ability to reproduciblycreate the same random streams. This requirement can be met via anopen-ended number of solutions, from pre-generating data, resettingimplementations, or building URNG primitives to support thisrequirement. These primitives lay the groundwork for building dynamicdigital protocols.

Any given source file contains a pool of uncertainty, and a time model.The timestamp determines the seed values within the pool of uncertainty,defining which instance of a URNG will be created. Therefore, anytimestamp represents an addressing function into all possible URNGinstances. While each URNG instance is limited to producing one randomstream, many different streams can be produced from the same pool ofuncertainty. The following are supporting primitives:

Static URNG Primitive

The “Static UNRG” creates an URNG instance based on the pool ofuncertainty found in the source file. Each unique timestamp creates adifferent instance of the URNG while sharing the same data.

t_urng*static_urng (char*source_name, t_timestamp timestamp);

Dynamic URNG Primitive

The “Dynamic URNG” takes the given source file and timestamp to create anew pool of uncertainty. This new pool of uncertainty and timestampdefines this dynamic instance of the URNG. Therefore, each uniquetimestamp creates a unique random number generator, which in turncreates a unique random stream.

t_urng*dynamic_urng (char*source_name, t_timestamp timestamp);

The same source file, timestamp, and above primitives create the sameinstance of the URNG, and subsequently produces the same random stream.Once a given URNG instance is defined, then the data plane has beenassigned.

Clone URNG Primitive

The “Clone URNG” creates a copy of the given URNG instance. Each clonewill produce the same random stream from the point of cloning.

t_urng*clone_urng (t_urng*urng);

URNG Value Primitive

The “URNG_value” is the standard URNG interface primitive for obtainingrandom values.

r_value urng_value (t_urng*urng);

Random Sub-Streams—Indexed URNG Primitive

The “indexed URNG” is the interface primitive for obtaining the nextrandom value in the “indexed” stream. Each time that the index changes,the index stream resets. This “resetting” allows the indexed URNG to bereused and thus reproduce any indexed stream. An example would entailsetting the index to a temp value, and then restoring back to theprevious index, so that the same index stream would start reproducingthe same random stream. The use of the clone URNG interface primitive,in conjunction with the indexed URNG interface primitive, allows anynumber of (different) simultaneous random streams to be supported fromthe same pool of uncertainty.

r_value indexed_urng (t_urng*urng, int32_t index);

The Virtual Cut—Memory Address+Offset

For example, adding an offset of the memory address used to read thedata pool(s) has the same effect as moving the origin of the memoryrange. This is similar to “cutting” the deck in a card game.

The Virtual Shuffle—Memory Address XOR Shuffle Value

The act of a bit-wise XOR of a Shuffle Value with a memory address iscomparable to the effect of a quick shuffle in place—the memory rangehas been reordered by the XOR operation. One can shuffle then cut, orone can cut then shuffle. These virtual card tricks can be performed asmany times as one wishes.

Random Sub-Streams—Using The Memory Address Offset

Manipulating the memory address can have some very useful side effects.Normally, the uncertainty random value generator would only produce onerandom stream. If each memory address used within the uncertainty randomvalue generator had an offset added to it, then a different randomsub-stream would be produced for each different offset. Any number ofarbitrary random sub-streams can be produced via selective addition ofmemory-offset values. This gives us a randomly addressable sub-streamwhenever it is needed. This tool is very useful in many applications.

The Identification of Source Files and URNG Instance

In most cases, the naming of a “file” generally requires firstidentifying where it can be found (path name) within some form of “namespace”. Typically, only the last part of the location is used to namethe file. While the path name specifies the external source fileidentification, additional internal identification exists: a creationtimestamp and a non-unique identifier. Between the external and internalidentification, any source file should be located within known systems.Once the correct source file has been found, a dynamic identifier (openfile ID) can be defined.

As source files and timestamps are employed to create URNG instances,they are given dynamic URNG instance identifiers. As clone instances arecreated, they are also given additional identifiers. Clearly, stochasticprocessing applications will have to manage and track these URNGinstance identifiers.

In hardware applications, the loading of the pool of uncertainty, andinitialization of address generators completes the identification of aURNG instance. Each additional addressing generator that supports “cut”(with or without “shuffle”) provides the means for indexed URNGs.

The Synchronization of Producers and Consumers

So long as the producer and consumer of data structures start with thesame source file and timestamp, they have reached the first level ofsynchronization by creating the same instances of the URNG. Thestochastic processing application(s) generally must define values(sub-stream identifiers) that represent distinct random sequences. Thesesub-stream identifiers become the “index” values of the indexed URNGprimitive. Each time the stochastic processing application generallyrequires a new random sub-stream, it can just create a new index(stream) value. This index value may or may not be in the datastructure. For example, some Internet protocols have “sequence numbers”embedded in the protocol definition; these sequence numbers can bemapped into index values. These index values become a fine grainaddressing (time) function for randomness. Clearly, while most protocolsequence numbers are ordered for a reason, these index values do notneed to be contiguous. So long as the algorithm for creating andmanaging these indexes is correct, the indexed URNG primitive will givethe same random sub-stream for any given (valid) index.

Starting index values can be explicitly or implicitly defined by theapplication. A transaction accounting number could be an example of anexplicitly defined index, while the third attachment in an email couldbe an implicit example. However, any application specific algorithm cancreate any (valid) index values.

Application-Specific Collections—Hash Functions, Breaking and RestoringProcesses

The task of protecting the fragile (static) data structures withindigital protocols may require up to two different application-specificcollections of processing elements. The first collection is some form of“hash” or Cyclic Redundancy Check (CRC) function that reduces a datastructure into a single value. If needed, the second collection is usedto invoke the data structure protection paradox.

The possibilities of “breaking and restoring” static data structures areinfinite, so it will remain an open-ended solution space. Any reversiblemeans to mangle the static data structure . . . and then restore it isequally effective. This list of examples to process static datastructures represents just a hint of possible solutions. Once the staticdata structure is created, then any combination of the following can beemployed: bit flipping of any size, bit swapping of any size, data ofany size injected and then removed, rotating any number of bits left orright (within any size unit), reversing bit order of (byte, 16-bit,word), etc. As an open-ended solution space, many more solutions can becreated.

The reversible morphing of a static data structure can be insertedbetween many existing hardware and/or software solutions. Thisprotection upgrade can be accomplished for relatively low cost to ordisruption of existing systems.

Another means to protect fragile data structures is to directly movetowards dynamic data structures. For example, many different datastructures can be overlapped (formatted) via a “variant record” means.In this way, the same overall data can be stored in roughly the sameamount of space. Within each variant record, the only significantdifference is that the same fields are stored in a different order. Bothproducer and consumer would have to synchronize whichever variant is tobe used this time. The ideal synchronization would be selection of avariant record (or selection of a routine to construct a variant record)based on uncertain data. In this way, over time, the same fields withinthe data structure would appear to be constantly moving. This variantrecord solution does not require the “breaking and restoring”collection, but does require reworking the hardware/software accesses tothe fields within the data structures.

The incorporation of breaking the certainty of existing static datastructures should be so low cost that it can become almost ubiquitous.In this way, we gain security of digital protocols without anysubstantial overhead. The second collection (hash function or CRC) givesan option that may carry even lower overhead.

Upgrading The CRC Metaphor To Become The Ross Integrity Check (RIC)

Many Internet protocols employ a CRC function to detect damaged(invalid) packets. If the CRC check fails, then the packet is discarded.These protocols naturally replace the missing packet and the only resultis a temporary glitch. There are many different CRC functions currentlydeployed throughout the network. However, in order to function asintended, each invocation of a current CRC generally must use thecorrect function. This defines the current CRC metaphor.

So, we can change the metaphor with a simple question—what happens ifthe wrong CRC function was invoked? In this case, the protocol is brokenand each request is discarded. If this consistent failure of a CRC checkis the correct intent, then the CRC value has become a failed “digitalsignature”. Unfortunately, if the CRC function becomes known, then theCRC value could be hacked to an extent that it passes the CRC check. Tocomplete the solution and prevent hacking, we generally must incorporateuncertain data into the CRC computation. This required uncertain data(of uncertain size) could only be produced by the correct instance of anURNG. Thus, the collection of hash/CRC functions plus additionaluncertain data actually upgrades the simple CRC metaphor to become asecure digital signature for each packet. Therefore, any unauthorizedpackets are ignored. This RIC replacement of the CRC metaphor remainslow cost enough to become ubiquitous.

Given an uncertain selection of a hash/CRC function from the collectionand an uncertain data addendum of uncertain size, we compute the datastructure plus addendum hash/CRC RIC value. The service provider selectsthe same hash/CRC function, creates the same uncertain addendum, andthen computes the RIC value. The service is only provided if theauthentication matches the value found in the data structure.

Applications of the RIC

Clearly, the RIC could be added to any digital protocol to provide a lowcost means for authentication. Many services are provided withoutauthentication, while others have very complex infrastructures tosupport authentication (Secure Socket Level SSL for instance). If theone time data exchange has been completed, then we have also exchangedrandom number generators. Therefore, we have also set up the minimalrequired infrastructure to support authentication. Assuming sharedstochastic processing application(s) are used via this data exchange,then dynamic custom applications in either hardware or software havealso been exchanged.

Converting Public Addresses Into Private Addresses (Fine Grain AccessControl)

Let us assume that many service providers have known “public addresses”.Assuming these service providers are upgraded to support RICauthentication, then only authorized services will be provided, whileall other requests are ignored. Thus, the RIC becomes a form ofrevocable access control for the service. This access control has anopen-ended number of possible applications.

Here is a simple example of this effect: if my phone optionally supportsRIC authentication, then I could “open” my phone number during the day,while limiting (closing) access at night to only those that I gavepermission to call me. Another way to view the effect of the RIC is tothink of this as converting the public (known) address into a private(authorized only) address. Now, any email, phone number, control system,IP address, financial or Internet transaction, etc. can have fine-grainaccess control.

The lack of fine-grain revocable access control plagues manyapplications. For example, the typical access given to databases coversall records. Instead, many organizations would benefit from dynamicallylimiting access to only those records where a need-to-know has beenauthorized, while denying access to the rest of the database. Clearly,this fine-grain access control would be useful in healthcare and IRSorganizations. This type of access control is another wide-openapplication space.

Stochastic Scaffolding—The Same Distinct Breaking/Restoration Algorithm

The Breaking and Restoring (BR) process continues with the stochasticscaffolding framework. Each developer of BR applications will becreating a framework for the multi-step selection and invocation of BRprocessing elements. The limitations set upon programmatic controlvariables (number of steps, order of steps, selection of BR elements,etc.) are all defined by the data produced by the URNG.

While the synchronization is established with the correct URNG instance,each part of the stochastic scaffolding starts with the same distinctuncertain data. Each sub-step within the framework may requireadditional data parameters. Any parameter, data item, selection value,chaos instruction, etc. can be provided with the indexed URNG in aconsistent (and reproducible) manner.

Clearly, the framework will have to deal with restoring whateverbreaking was accomplished. Assuming that any new breaking processelement may affect the results of previous breaking process elements,then the restoration sequence will have to back out each break byrestoring them in reverse order. The processing of a “last in first out(LIFO)” stack is a classic, well-understood, algorithmic metaphor.

Assuming that both the BR process and RIC are employed, then the RICvalue is computed and saved within the data structure before any changesare made. Once the data structure is restored, then the RIC isrecomputed to validate the data structure and confirm authorization.

Optional use of Breaking/Restoration or RIC

The above section covers the case where both the BR process and RIC areemployed at the same time. However, there are many valid applications inwhich only one of them would be used. The RIC will stand alone in manydigital protocols as a low cost solution for access control. Thestandalone use of the BR process will be used in cases of software DRMs.If only the RIC is employed in this software DRM case, then that singlepoint of attack would be a tempting target to hack, thus overriding theeffect of the RIC. However, the standalone use of the BR process isstrong enough to protect the software. After all, any improperlyrestored software remains nonfunctional. There are an open-ended numberof applications that will only use either the BR process or the RIC.

Data Congruential Generator

“Linear Congruential Generators” produce deterministic values thatgenerally must be transformed into paradox safe values within the URNGimplementation. While the requirements for producing paradox safe domainvalues for the Uncertainty Function remain the same, the removal of theflawed PRNG simplifies the process with better randomness quality:

mask  = urng->pool[ prng (1) % urng-> modulo ]; unsafe_1 = prng (4);unsafe_2 = prng (5); domain  = (~mask & unsafe_1) | (mask & unsafe_2 ) %urng->modulo;

Starting with one (or more) nondeterministic value(s) from the pool ofuncertainty, we can generate a domain value from a prime modulooperation. Instead of a “Linear Congruential Generator”, we replace a“linear” equation with paradox safe data to produce the DataCongruential Generator (DCG). Given two random indexes (seed0, seed1)into the pool of uncertainty, here is the replacement C code example.

uint64_t   y; uint32_t   hi, low, domain; hi    = urng->pool[ seed0++ %urng->modulo ]; low   = urng->pool[ seed1−− % urng->modulo ]; y    =((uint64_t) hi << 32) | (low); domain = ( y % prime ) % urng->modulo;

Note: this embodiment uses the concatenation of two 32-bit values tocreate a 64-bit value. In lieu of concatenation, nearly any binaryoperation will function as well. While a single pool value isfunctional, it tends to repeat the same addressing sequence far tooquickly. By replacing the above “prime” number, one can create adifferent domain value generator. The DCG embodiment can be created ineither hardware of software.

“C header” This C header fills in some details missing in the above codesnippets #define MAX_REPS 16 #define INSTRUCTIONS_PER_WORD 8 #defineINSTRUCTION_MASK 0xF #define INSTRUCTION_SHIFT 4#defineNUM_STOCHASTIC_POINTS 7 #define prng( num ) ((*urng->prng[ num].PRNG)( &urng->prng[ num ].state )) typedef  uint32_t  r_value;  //base type of uncertainty value // generic PRNG function typedef r_value(*PRNG_function)(r_value* seed); // generic Random Edit Process typedefr_value (*edit_process)(r_value, r_value, r_value); typedef struct { PRNG_function PRNG;  r_value state; } t_prng; // STRUCTURE : t_chaos //// Chaos CPU //   instruction (block) currently 4 bits each //   PCProgram Counter within instruction block //   16 CPU operations indexedvia 4 bit instruction //   each operation is a generic Random EditProcess //   each operation is randomly selected via uncertainty valuefrom REP table // // Since each instruction block is randomly fishedfrom pool of uncertainty // and then used to perform random operationsagainst other random // streams, the Chaos Engine is an appropriatename. typedef struct { r_value instruction; // random value holdinginstructions r_value PC; // current instruction counter within aboveinstruction edit_process operation[ MAX_REPS ]; // table of chaosoperations (Random Edit Processes) } t_chaos; typedef struct { uint32_t poolsize; // allocation size in r_values uint32_t  modulo; // primenumber addressing modulo t_prng  prng[ NUM_STOCHASTIC_POINTS ]; // PRNG addressing functions t_chaos  cpuAdr0; // address chaos engine t_chaos cpuAdr1; // address chaos engine t_chaos  cpudata; // data chaos enginer_value *pool;     // data pool pointer } t_curng; typedef struct {uint32_t  poolsize; // allocation size in r_values uint32_t  modulo; //prime number addressing modulo t_prng  prng[ NUM_STOCHASTIC_POINTS ]; //PRNG  addressing functions r_value *pool; // data pool pointer } t_urng;

It is understood that the above-described embodiments are onlyillustrative of the application of the principles of the presentinvention. The present invention may be embodied in other specific formswithout departing from its spirit or essential characteristics. Thedescribed embodiment is to be considered in all respects only asillustrative and not restrictive. The scope of the invention is,therefore, indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

For example, although the above discussion describes particular uses forsuch systems, methods and etc., it is understood that the applicationsare plethoric and in some cases unknowable at this point.

Additionally, although the figures illustrate specific connections,relationships, and sequences, it is understood that the plethoricconnections, relationships and sequences not described by but also notcontraindicated by the claims are envisioned and may be implemented inone or more non-limiting embodiments of the invention.

Thus, while the present invention has been fully described above withparticularity and detail in connection with what is presently deemed tobe the most practical and preferred embodiment of the invention, it willbe apparent to those of ordinary skill in the art that numerousmodifications, including, but not limited to, variations in size,materials, shape, form, function and manner of operation, assembly anduse may be made, without departing from the principles and concepts ofthe invention as set forth in the claims. Further, it is contemplatedthat an embodiment may be limited to consist of or to consistessentially of one or more of the features, functions, structures,methods described herein.

1. A system of stochastic processing of information using a computingdevice, comprising: a) an architect module including a processor, thearchitect module configured to manage and control stochastic processingof data; b) a non-deterministic data pool module functionally coupled tothe architect module and configured to provide a stream ofnon-deterministic values that are not derived from a function; c) aplurality of functionally equivalent data processing modulesfunctionally coupled to the architect module, each configured tostochastically process data as called upon by the architect module; d) adata feed module in functional communication with the architect moduleand configured to feed a data set desired to be stochasticallyprocessed; and e) a structure memory module including a memory storagedevice, the structure memory module coupled to the architect module andconfigured to provide sufficient information for the architect module toduplicate a predefined processing architecture and to record a utilizedprocessing architecture.